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Auto-repair loop (bounded)

auto_repair_loop

Automate TouchDesigner cook-error repair by scanning a subtree, clustering errors, routing to fixers, and iterating until clean or stalled, with dry-run planning.

Instructions

Driver: scan a subtree for cook errors, cluster them, route each cluster to the right fix (calls repair_network for structural/expression/flag issues; surfaces fix_shader / fix_reactivity as prompt hand-offs the agent must execute next turn), re-check, and iterate until clean, no-progress (stalled), or max_iterations (exhausted). Dry-run by default — one planning iteration, no writes. The loop CANNOT fix shaders or dead reactivity itself; it points the agent at them via recommended_prompts. Returns {status, iterations[], errors_before, errors_after, remaining[], recommended_prompts[], warnings}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoRoot of the subtree to scan + repair./project1
dry_runNoWhen true (default), PLAN routes only (no writes). Propagated to repair_network; the loop runs exactly one iteration in dry-run mode.
min_progressNoConvergence threshold — if an iteration clears fewer than this many errors, the loop stops (stalled).
allowed_fixersNoSubset of fixers the loop may route to. Drop 'repair_network' to make the loop advisory only (prompts + remaining, no writes).
max_iterationsNoHard cap on outer iterations — each iteration = one scan + one route + one apply.
include_warningsNoWhen true, treat 'warning' severity errors as in-scope. Default ignores warnings (no-op until the bridge surfaces severity).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only state readOnly=false, openWorld=true, destructive=false. The description adds substantial behavioral detail: dry-run does no writes and runs exactly one planning iteration, non-dry-run propagates to repair_network, loop stops on clean/stalled/exhausted, and it returns a structured result. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: it starts with an overview, then adds dry-run behavior, a key limitation, and the return envelope. Each sentence contributes non-redundant information without bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even with no output schema, the description fully specifies the return shape {status, iterations[], errors_before, errors_after, remaining[], recommended_prompts[], warnings} and explains termination conditions and fixer hand-offs. For a complex orchestrator, this is sufficient for an agent to invoke and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with already detailed parameter descriptions, so baseline is 3. The description enriches meaning by tying dry_run to one planning iteration with no writes, allowed_fixers to advisory-only behavior, and max_iterations to the 'exhausted' termination state.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Driver: scan a subtree for cook errors...' and clearly describes a bounded iterative repair loop. It explicitly specifies the fixers it calls (repair_network) and those it only hands off (fix_shader/fix_reactivity), distinguishing it from sibling fix and repair tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states when the loop is appropriate (subtree cook errors), its iteration bounds, dry-run default, and explicitly notes that shaders/dead reactivity are not fixed directly but surfaced via recommended_prompts for the agent to execute next turn. This gives clear routing guidance versus repair_network and fix_shader/fix_reactivity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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